Rank Aggregation Algorithm Selection Meets Feature Selection

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• 关键词：Meta ; learning ; Rank aggregation ; Ensemble learning ; Feature selection
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9729
• 期：1
• 页码：740-755
• 全文大小：700 KB
• 参考文献：1.Albert, M.H., Aldred, R.E., Atkinson, M.D., van Ditmarsch, H.P., Handley, B., Handley, C.C., Opatrny, J.: Longest subsequences in permutations. Australasian Journal of Combinatorics 28, 225–238 (2003)MathSciNet MATH
  2.Bachmaier, C., Brandenburg, F.J., Gleißner, A., Hofmeier, A.: On maximum rank aggregation problems. In: Lecroq, T., Mouchard, L. (eds.) IWOCA 2013. LNCS, vol. 8288, pp. 14–27. Springer, Heidelberg (2013)CrossRef
  3.Benner, P., Mehrmann, V., Sorensen, D.C.: Dimension Reduction of Large-Scale Systems, vol. 45. Springer, Heidelberg (2005)MATH
  4.Bolón-Canedo, V., Sánchez-Maroño, N., Alonso-Betanzos, A., Benítez, J., Herrera, F.: A review of microarray datasets and applied feature selection methods. Information Sciences 282, 111–135 (2014)CrossRef
  5.de Borda, J.C.: Mémoire sur les élections au scrutin (1781)
  6.Brazdil, P., Carrier, C.G., Soares, C., Vilalta, R.: Metalearning: Applications to Data Mining. Springer Science & Business Media (2008)
  7.Burkovski, A., Lausser, L., Kraus, J.M., Kestler, H.A.: Rank aggregation for candidate gene identification. In: Data Analysis, Machine Learning and Knowledge Discovery, pp. 285–293. Springer (2014)
  8.Copeland, A.H.: A reasonable social welfare function. In: Seminar on Applications of Mathematics to Social Sciences. University of Michigan (1951)
  9.Das, S., Das, A.K.: Sample classification based on gene subset selection. In: Behera, H.S., Mohapatra, D.P. (eds.) Computational Intelligence in Data Mining. AISC, vol. 410, pp. 227–236. Springer, India (2015)CrossRef
  10.DeConde, R.P., Hawley, S., Falcon, S., Clegg, N., Knudsen, B., Etzioni, R.: Combining results of microarray experiments: a rank aggregation approach. Statistical Applications in Genetics and Molecular Biology 5(1) (2006)
  11.Deza, M., Huang, T.: Metrics on permutations, a survey. Journal of Combinatorics, Information and System Sciences. Citeseer (1998)
  12.Dietterich, T.G.: Ensemble methods in machine learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1–15. Springer, Heidelberg (2000)CrossRef
  13.Dwork, C., Kumar, R., Naor, M., Sivakumar, D.: Rank aggregation methods for the web. In: Proceedings of the 10th International Conference on World Wide Web, pp. 613–622. ACM (2001)
  14.Filchenkov, A., Pendryak, A.: Datasets meta-feature description for recommending feature selection algorithm. In: AINL-ISMW FRUCT, pp. 11–18 (2015)
  15.Fisher, R.A., Yates, F., et al.: Statistical tables for biological, agricultural and medical research. Statistical Tables for Biological, Agricultural and Medical Research 13(Ed. 6.) (1963)
  16.Garner, S.R., et al.: Weka: the waikato environment for knowledge analysis. In: Proceedings of the New Zealand Computer Science Research Students Conference, pp. 57–64. Citeseer (1995)
  17.Giraud-Carrier, C.: Metalearning-a tutorial. In: Proceedings of the 7th International Conference on Machine Learning and Applications, pp. 1–45 (2008)
  18.Guyon, I., Gunn, S., Nikravesh, M., Zadeh, L.A.: Feature Extraction: Foundations and Applications, vol. 207. Springer (2008)
  19.Jones, N.C., Pevzner, P.: An Introduction to Bioinformatics Algorithms. MIT press (2004)
  20.Kekre, H.B., Shah, K.: Performance Comparison of Kekre’s Transform with PCA and Other Conventional Orthogonal Transforms for Face Recognition, pp. 873–879. ICETET (2009)
  21.Kent, J.T.: Information gain and a general measure of correlation. Biometrika 70(1), 163–173 (1983)MathSciNet CrossRef MATH
  22.Rice, J.R.: The Algorithm Selection Problem (1975)
  23.Saeys, Y., Inza, I., Larranaga, P.: A review of feature selection techniques in bioinformatics. Bioinformatics 23(19), 2507–2517 (2007)CrossRef
  24.Schalekamp, F., van Zuylen, A.: Rank aggregation: together we’re strong. In: Proceedings of the Meeting on Algorithm Engineering & Expermiments, pp. 38–51. Society for Industrial and Applied Mathematics (2009)
  25.Smetannikov, I., Filchenkov, A.: Melif: filter ensemble learning algorithm forgene selection. In: Advanced Science Letters (2016, to appear)
  26.Wang, G., Song, Q., Sun, H., Zhang, X., Xu, B., Zhou, Y.: A feature subset selection algorithm automatic recommendation method. Journal of Artificial Intelligence Research 47(1), 1–34 (2013)MATH
  27.Wang, R., Utiyama, M., Goto, I., Sumita, E., Zhao, H., Lu, B.L.: Converting continuous-space language models into n-gram language models with efficient bilingual pruning for statistical machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing 15(3), 11 (2016)CrossRef
  28.Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1(1), 67–82 (1997)CrossRef
  29.Zabashta, A., Smetannikov, I., Filchenkov, A.: Study on meta-learning approach application in rank aggregation algorithm selection. In: MetaSel Workshop at ECML PKDD 2015, pp. 115–117 (2015)
  
• 作者单位：Alexey Zabashta (14)
  Ivan Smetannikov (14)
  Andrey Filchenkov (14)
  
  14. Computer Science Department, ITMO University, 49 Kronverksky Pr., 197101, St. Petersburg, Russia
  
• 丛书名：Machine Learning and Data Mining in Pattern Recognition
• ISBN：978-3-319-41920-6
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9729

文摘

Rank aggregation is the important task in many areas, and different rank aggregation algorithms are created to find optimal rank. Nevertheless, none of these algorithms is the best for all cases. The main goal of this work is to develop a method, which for each rank list defines, which rank aggregation algorithm is the best for this rank list. Canberra distance is used as a metric for determining the optimal ranking. Three approaches are proposed in this paper and one of them has shown promising result. Also we discuss, how this approach can be applied to learn filtering feature selection algorithm ensemble.